This disclosure relates to the generation and use of image digests to locate images with matching image content.
Due to the popularity of digital technology, more and more digital images are being created and stored every day. The increasing volume of digital images being produced introduces problems for managing image repositories. For example, a user cannot determine if an image already exists in an image repository without exhaustively searching through all the existing images stored in the image repository. Further complication may arise from the fact that two images that appear identical to the human eye may have different digital representations, e.g. an original image and its compressed version, an image stored using distinct transforms, or an image enhanced via common signal processing operations, thus making it difficult to use automated methods to locate matching images.
Further, some copies of an original image may have been cropped, compressed, resized and/or enhanced. Other copies may have been rotated, or may have been generated with modified control parameters, such as a higher or lower contrast ratio setting than were used to generate the original image. Further, content items within the images may have been manually or electronically edited to add or remove small features within the photograph.
Generally, stored images may go through several distortions and these distorted versions may be either archived in image repositories or made available as query images for use in locating an original or otherwise related image. Recent research in image hashes/digests has addressed this problem to some extent. An image digest is simply a function of the image content that evaluates to a vector that is relatively short, as compared with the image size. For example, see M. Schneider and S. F. Chang, “A robust content based digital signature for image authentication,” Proc. IEEE Conf. on Image Processing, vol. 3, pp. 227-230, September 1996; R. Venkatesan, S. M. Koon, M. H. Jakubowski, and P. Moulin, “Robust Image Hashing,” Proc. IEEE Conf. on Image Processing, pp. 664-666, September 2000; and V. Monga and B. L. Evans, “Robust Perceptual Image Hashing Using Feature Points,” Proc. IEEE Conf. on Image Processing, 2004.
Previous research has focused on the creation of image digests that are robust under common signal and image processing operations, while geometric distortions such as the cropping of an image, translation of image pixels etc. have not been addressed. Creating an image digest using traditional cryptographic or repository hashes poses a problem in that such image digests are sensitive to very small changes to the image data. For example, current image digests that result from perceptually similar images may not be sufficiently similar for use in identifying the images as perceptually similar.
Such approaches are not able to accommodate cropping and pixel translations, such as rotation. Further, cropping and pixel translation are quite common in typical repository images. For example, consider many shots of the same scene taken by a physically moving the camera and/or by adjusting the focal length, i.e. zoom factor. In addition, printing and scanning often may involve mild rotation and significant cropping of the image.